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                    <h1 class="description center-align post-title">tf.get_variable()和tf.variable的区别</h1>
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                <p> tf.variable()和tf.getvariable()两种方法的使用。<span id="more"></span> </p>
<p>&emsp;&emsp;<strong>tf.variable()用于生成一个初始值为initial-value的变量，必须指定初始化值。</strong></p>
<p>&emsp;&emsp;<strong>tf.getvariable()获取已经存在的变量(要求不仅名字，而且初始化方法等各个参数都一样)，如果不存在这个变量，则会新建一个；可以用各种初始化的方法，不用明确指定值。</strong></p>
<h1 id="一、tf-get-variable-权值共享"><a href="#一、tf-get-variable-权值共享" class="headerlink" title="一、tf.get_variable()权值共享"></a>一、tf.get_variable()权值共享</h1><p>&emsp;&emsp;<strong>简述：</strong>权值共享是指一些需要共享的变量，如神经网络里面的权值，embedding_matrix这种变量，则可用tf.variable()方法。tf.variable()和tf.getvariable()是有区别的，区别如下：</p>
<pre class="line-numbers language-python"><code class="language-python">get_variable<span class="token punctuation">(</span>
    name<span class="token punctuation">,</span>
    shape<span class="token operator">=</span>None<span class="token punctuation">,</span>
    dtype<span class="token operator">=</span>None<span class="token punctuation">,</span>
    initializer<span class="token operator">=</span>None<span class="token punctuation">,</span>
    regularizer<span class="token operator">=</span>None<span class="token punctuation">,</span>
    trainable<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">,</span>
    collections<span class="token operator">=</span>None<span class="token punctuation">,</span>
    caching_device<span class="token operator">=</span>None<span class="token punctuation">,</span>
    partitioner<span class="token operator">=</span>None<span class="token punctuation">,</span>
    validate_shape<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">,</span>
    use_resource<span class="token operator">=</span>None<span class="token punctuation">,</span>
    custom_getter<span class="token operator">=</span>None<span class="token punctuation">,</span>
    constraint<span class="token operator">=</span>None
<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p>&emsp;&emsp;<strong>参数详解：</strong></p>
<p>&emsp;&emsp;<code>name:</code>新变量或现有变量的名称。<br>&emsp;&emsp;<code>shape:</code>新变量或现有变量的形状。<br>&emsp;&emsp;<code>dtype:</code>新变量或现有变量的类型。<br>&emsp;&emsp;<code>ininializer:</code>如果创建了，则用它初始化。<br>&emsp;&emsp;<code>regularizer：</code>A（Tensor - &gt; Tensor或None）函数;将它应用于新创建的变量的结果将添加到集合tf.GraphKeys.REGULARIZATION_LOSSES中，并可用于正则化。<br>&emsp;&emsp;<code>trainable：</code>如果为True，还将变量添加到图形集合GraphKeys.TRAINABLE_VARIABLES（参见tf.Variable）。<br>&emsp;&emsp;<code>collections：</code>要将变量添加到的图表集合列表。默认为[GraphKeys.GLOBAL_VARIABLES]（参见tf.Variable）。<br>&emsp;&emsp;<code>caching_device：</code>可选的设备字符串或函数，描述变量应被缓存以供读取的位置。默认为Variable的设备。如果不是None，则在另一台设备上缓存。典型用法是在使用变量驻留的Ops的设备上进行缓存，以通过Switch和其他条件语句进行重复数据删除。<br>&emsp;&emsp;<code>partitioner：</code>可选callable，接受完全定义的TensorShape和要创建的Variable的dtype，并返回每个轴的分区列表（当前只能对一个轴进行分区）。<br>&emsp;&emsp;<code>validate_shape：</code>如果为False，则允许使用未知形状的值初始化变量。如果为True，则默认为initial_value的形状必须已知。<br>&emsp;&emsp;<code>use_resource：</code>如果为False，则创建常规变量。如果为true，则使用定义良好的语义创建实验性ResourceVariable。默认为False（稍后将更改为True）。在Eager模式下，此参数始终强制为True。<br>&emsp;&emsp;<code>custom_getter：</code>Callable，它将第一个参数作为true getter，并允许覆盖内部get_variable方法。 custom_getter的签名应与此方法的签名相匹配，但最适合未来的版本将允许更改：def custom_getter（getter，* args，** kwargs）。也允许直接访问所有get_variable参数：def custom_getter（getter，name，* args，** kwargs）。一个简单的身份自定义getter只需创建具有修改名称的变量是：python def custom_getter（getter，name，* args，** kwargs）：return getter（name +’_suffix’，* args，** kwargs）</p>
<p>&emsp;&emsp;<strong>注意：</strong>如果initializer初始化方法是None(默认值),则会使用variable_scope()中定义的initializer,如果也为None,则默认使用glorot_uniform_initializer,也可以使用其他的tensor来初始化，value,和shape与此tensor相同</p>
<p>&emsp;&emsp;正则化方法默认是None,如果不指定，只会使用variable_scope()中的正则化方式，如果也为None，则不使用正则化；</p>
<p>&emsp;&emsp;<strong>附：</strong> tf.truncated_narmal()和tf.truncated_naomal__initializer()的区别</p>
<p>&emsp;&emsp;tf.truncated_narmal(shape=[],mean=0,stddev=0.5)使用时必须制定shape,返回值是在截断的正态分布随机生成的指定shape的tensor<br>&emsp;&emsp;tf.truncated_normal_initializer(mean=0.stddev=0.5)调用返回一个initializer 类的一个实例（就是一个初始化器），不可指定shape。</p>
<h1 id="二、tf-variable"><a href="#二、tf-variable" class="headerlink" title="二、tf.variable()"></a>二、tf.variable()</h1><p>&emsp;&emsp;本质上也是一个tensor张量，variable主要用于数据存储。在计算图的运算过程中，值会一直保存到程序运行结束。<strong>所有和variable有关的操作在计算的时候都需要session会话来控制</strong>，包括计算，打印。</p>
<pre class="line-numbers language-python"><code class="language-python">tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>
    initial_value<span class="token operator">=</span>None<span class="token punctuation">,</span> 
    trainable<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">,</span> 
    collections<span class="token operator">=</span>None<span class="token punctuation">,</span> 
    validate_shape<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">,</span> 
    caching_device<span class="token operator">=</span>None<span class="token punctuation">,</span> 
    name<span class="token operator">=</span>None<span class="token punctuation">,</span> 
    variable_def<span class="token operator">=</span>None<span class="token punctuation">,</span> 
    dtype<span class="token operator">=</span>None<span class="token punctuation">,</span> 
    expected_shape<span class="token operator">=</span>None<span class="token punctuation">,</span> 
    import_scope<span class="token operator">=</span>None
<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p>&emsp;&emsp;<strong>参数详解：</strong></p>
<p>&emsp;&emsp;<code>initial_value：</code>Tensor或可转换为Tensor的Python对象，它是Variable的初始值。除非validate_shape设置为False，否则初始值必须具有指定的形状；也可以是一个可调用的，没有参数，在调用时返回初始值。在这种情况下，必须指定dtype。 （请注意，init_ops.py中的初始化函数必须首先绑定到形状才能在此处使用。）<br>&emsp;&emsp;<code>trainable：</code>如果为True，则会默认将变量添加到图形集合GraphKeys.TRAINABLE_VARIABLES中。此集合用于Optimizer类优化的的默认变量列表【可为optimizer指定其他的变量集合】，可就是要训练的变量列表。<br>&emsp;&emsp;<code>collections：</code>一个图graph集合列表的关键字。新变量将添加到这个集合中。默认为[GraphKeys.GLOBAL_VARIABLES]。也可自己指定其他的集合列表。<br>&emsp;&emsp;<code>validate_shape：</code>如果为False，则允许使用未知形状的值初始化变量。如果为True，则默认为initial_value的形状必须已知。</p>
<p>&emsp;&emsp;<code>caching_device：</code>可选设备字符串，描述应该缓存变量以供读取的位置。默认为Variable的设备。如果不是None，则在另一台设备上缓存。典型用法是在使用变量驻留的Ops的设备上进行缓存，以通过Switch和其他条件语句进行重复数据删除。<br>&emsp;&emsp;<code>name：</code>变量的可选名称。默认为“Variable”并自动获取。<br>&emsp;&emsp;<code>variable_def：</code>VariableDef协议缓冲区。如果不是None，则使用其内容重新创建Variable对象，引用图中必须已存在的变量节点。图表未更改。variable_def和其他参数是互斥的。<br>&emsp;&emsp;<code>dtype：</code>如果设置，则initial_value将转换为给定类型。如果为None，则保留数据类型（如果initial_value是Tensor），或者convert_to_tensor将决定。<br>&emsp;&emsp;<code>expected_shape：</code>TensorShape。如果设置，则initial_value应具有此形状。<br>&emsp;&emsp;<code>import_scope：</code>可选字符串。要添加到变量的名称范围。仅在从协议缓冲区初始化时使用。<br>&emsp;&emsp;一般常用的参数包括初始化值和名称name(是该变量的唯一索引)，在使用变量之前必须要进行初始化，初始化的方式有三种：<br>&emsp;&emsp;&emsp;&emsp;在会话中运行initializer操作。<br>&emsp;&emsp;&emsp;&emsp;从文件中恢复，如restore from checkpoint。<br>&emsp;&emsp;&emsp;&emsp;自己通过tf.assign()给变量附初值。</p>
<h5 id="补充："><a href="#补充：" class="headerlink" title="补充："></a>补充：</h5><ul>
<li><p><strong>get_variable示例代码：</strong></p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token keyword">with</span> tf<span class="token punctuation">.</span>Session<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">as</span> sess<span class="token punctuation">:</span>   
    embedding_matrix <span class="token operator">=</span> tf<span class="token punctuation">.</span>get_variable<span class="token punctuation">(</span><span class="token string">'embedding_matrix'</span><span class="token punctuation">,</span> shape<span class="token operator">=</span>config<span class="token punctuation">.</span>wordvectors<span class="token punctuation">.</span>shape<span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">,</span> trainable<span class="token operator">=</span><span class="token boolean">False</span><span class="token punctuation">)</span><span class="token punctuation">.</span>assign<span class="token punctuation">(</span>config<span class="token punctuation">.</span>wordvectors<span class="token punctuation">)</span>   <span class="token comment" spellcheck="true"># assign是tensorflow中的赋值操作。get_variable是获得已经存在的变量和创建一个变量。</span>
    emb_mtx <span class="token operator">=</span> sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>embedding_matrix<span class="token punctuation">)</span>  <span class="token comment" spellcheck="true"># 运行上一个句子，将config.wordvectors的值赋给embedding_marix。又保留embedding_matrix的维度。</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span></span></code></pre>
</li>
<li><p><strong>variable示例代码：</strong></p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token comment" spellcheck="true"># 示例代码</span>
<span class="token keyword">import</span> tensorflow <span class="token keyword">as</span> tf
a<span class="token operator">=</span>tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">)</span>
b<span class="token operator">=</span>tf<span class="token punctuation">.</span>constant<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">)</span>  <span class="token comment" spellcheck="true">#定义变量a=0和常量1,并且定义a的自加运算aplus1和更新a的值得操作update"""</span>
aplus1<span class="token operator">=</span>tf<span class="token punctuation">.</span>add<span class="token punctuation">(</span>a<span class="token punctuation">,</span>b<span class="token punctuation">)</span>
update<span class="token operator">=</span>tf<span class="token punctuation">.</span>assign<span class="token punctuation">(</span>a<span class="token punctuation">,</span>aplus1<span class="token punctuation">)</span>

init<span class="token operator">=</span>tf<span class="token punctuation">.</span>initialize_all_variables<span class="token punctuation">(</span><span class="token punctuation">)</span>   <span class="token comment" spellcheck="true">#设置完变量之后，必须要对变量进行初始化工作，变量才能使用</span>
<span class="token keyword">with</span> tf<span class="token punctuation">.</span>Session<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">as</span> sess<span class="token punctuation">:</span>
    sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>init<span class="token punctuation">)</span> <span class="token comment" spellcheck="true">#运行初始化变量的计算</span>
    <span class="token keyword">for</span> i <span class="token keyword">in</span> range<span class="token punctuation">(</span><span class="token number">10</span><span class="token punctuation">)</span><span class="token punctuation">:</span> <span class="token comment" spellcheck="true">#循环10次，查看a逐渐自加1的结果</span>
        sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>update<span class="token punctuation">)</span> <span class="token comment" spellcheck="true">#执行变量a的自加运算</span>
        <span class="token keyword">print</span><span class="token punctuation">(</span>sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>a<span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token comment" spellcheck="true">#打印变量a也需要用session.run</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
</li>
<li><p><strong>Variable补充说明</strong></p>
<pre class="line-numbers language-python"><code class="language-python">b <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">)</span>  
<span class="token comment" spellcheck="true"># Variable：作为存储节点的变量（Variable）不是一个简单的节点，而是一副由四个字节点构成的子图。</span>
      <span class="token comment" spellcheck="true">#（1）量的初始值——initial_value。</span>
      <span class="token comment" spellcheck="true">#（2）更新变量值的操作op——Assign。</span>
      <span class="token comment" spellcheck="true">#（3）读取变量值的操作op——read</span>
      <span class="token comment" spellcheck="true">#（4）变量操作——（a）</span>
<span class="token comment" spellcheck="true">#上述四个步骤即：首先，将initial_value赋值（Assign）给节点，存储在（a）当中，当需要读取该变量时，调用read函数取值即可</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
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